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Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection

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Title: Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection
Authors: Maeda, Keisuke Browse this author
Kushima, Tetsuya Browse this author
Takahashi, Sho Browse this author →KAKEN DB
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Interest level estimation
behavior feature
similar user selection
tensor completion
missing value estimation
Issue Date: 8-Jul-2020
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Access
Volume: 8
Start Page: 126109
End Page: 126118
Publisher DOI: 10.1109/ACCESS.2020.3007963
Abstract: A method for estimating interest levels from behavior features via tensor completion including adaptive similar user selection is presented in this paper. The proposed method focuses on a tensor that is suitable for data containing multiple contexts and constructs a third-order tensor in which three modes are "products", "users" and "user behaviors and interest levels" for these products. By complementing this tensor, unknown interest level estimation of a product for a target user becomes feasible. For further improving the estimation performance, the proposed method adaptively selects similar users for the target user by focusing on converged estimation errors between estimated interest levels and known interest levels in the tensor completion. Furthermore, the proposed method can adaptively estimate the unknown interest from the similar users. This is the main contribution of this paper. Therefore, the influence of users having different interests is reduced, and accurate interest level estimation can be realized. In order to verify the effectiveness of the proposed method, we show experimental results obtained by estimating interest levels of users holding books.
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article
Appears in Collections:総合IR本部 (Office of Institutional Research) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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